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Model comparison

Voyage Rerank 3 vs Jina Reranker v3.5

Compare Voyage Rerank 3 and Jina Reranker v3.5 using the same provider-sourced reranking & retrieval rubric. No mystery score and no invented benchmark ranking.

Facts checked September 4, 2026

Quick take

Voyage Rerank 3

Voyage AI's quality-focused preview reranker, billed by all query and candidate tokens processed in each request.

Best for

  • Teams already using Voyage embeddings
  • Token-metered retrieval pipelines
  • Evaluating a new quality-focused reranker

Watch out for

Treat it as a preview and confirm candidate caps, language behavior, retention, regional processing, and production support directly before launch.

Jina Reranker v3.5

Jina's compact listwise reranker for long, multilingual, structured, legal, financial, and domain-specific candidate sets.

Best for

  • Long candidate lists that benefit from listwise comparison
  • Legal, financial, multilingual, or structured retrieval
  • Air-gapped and controlled search infrastructure

Watch out for

The open-weight license is non-commercial, not Apache or MIT. Provider latency figures use specific A100 test setups and should not be treated as your production SLA.

Compare the published facts

Voyage Rerank 3 vs Jina Reranker v3.5

Values use each provider's own published units and limits. A blank means the provider did not publish a directly comparable value in the sources reviewed.

Reranking & retrievalVoyage Rerank 3Jina Reranker v3.5
Price basisThe provider's billing unit, with the document or token assumptions needed to compare it fairly.$0.05 / 1M processed tokensToken packages; no single public dollar rate
Text capacityHow much query and candidate text the model can consider in one scoring pass.32,000 tokens131K tokens shared by query and candidates
Candidates per requestThe maximum number of possible results accepted in one request, when the provider publishes it.Not published for the Rerank 3 previewNo fixed count published; bounded by shared context
Language coveragePublished multilingual support; an exact count is shown only when the provider gives one.Not yet detailed for the preview93 supported; trained on 24
Structured dataWhether the model is documented for JSON, tables, XML, or other data beyond normal prose.Text candidates; structured-data support not statedTables, JSON, XML, legal and domain text
Where it runsHosted API, cloud marketplace, private deployment, or self-hosted weights.Voyage hosted API and SDKsJina API, Elastic, clouds, Hugging Face, air-gapped

How to choose

Compare the job, not the hype.

Start with the job you need to complete, then validate cost, access, and policy details on your exact provider route.

Voyage Rerank 3

The reviewed Rerank 3 and pricing documentation does not publish a complete prompt-retention or model-training commitment. Obtain current data-processing terms before sending private retrieval candidates.

Jina Reranker v3.5

Self-hosted and air-gapped deployments keep request data inside infrastructure you control. The reviewed hosted API pages do not state a complete retention or training-use commitment, so confirm Jina or Elastic terms for sensitive search data.

Frequently asked questions

Voyage Rerank 3 vs Jina Reranker v3.5 FAQ

What is the main difference between Voyage Rerank 3 and Jina Reranker v3.5?

Voyage Rerank 3: Voyage AI's quality-focused preview reranker, billed by all query and candidate tokens processed in each request. Jina Reranker v3.5: Jina's compact listwise reranker for long, multilingual, structured, legal, financial, and domain-specific candidate sets.

Should I choose Voyage Rerank 3 or Jina Reranker v3.5?

Consider Voyage Rerank 3 when your priority is Teams already using Voyage embeddings. Consider Jina Reranker v3.5 when your priority is Long candidate lists that benefit from listwise comparison. Test both with your own data and provider route before committing.

Is this Voyage Rerank 3 vs Jina Reranker v3.5 comparison based on Cody benchmarks?

No. This comparison aligns provider-published facts for the Reranking & retrieval category. It does not claim a universal winner or combine incompatible third-party benchmark scores.